Language education in the regions of Russia and Canada: A comparative analysis
Bibliographic record
Abstract
© 2016 Slavina.This article is focused on the language policy comparative analysis necessity in the sphere of education in the regions of Russia and Canada. The aim of the work is to identify the management risks and to use the studied regions positive experience meeting the challenges facing the policy and language planning in the Russian Federation regions. The comparative description method of the language support programs, the way for the implementation at the various institutional levels are the basis for the analysis in this article. The results showed that “The Tatar State Program”, developed by the Republic of Tatarstan, encompasses all contexts of language management such as regulatory and legal support; organizational and structural support; coordination and arranging of the scientific scholars, maintain resources for language training at all levels in the educational system; national- language environment for the family; public opinion institutions; resource provision. “Plan 2013” is a roadmap for the French language development as the sate language of Canada, and it is the tied more to the field of education, which trends to the modal of “bi or multilingual Canadian”. The results presented in the article can be useful as the recommendations to implement the regional language development in the educational sphere. The comparative analysis of the language policy maintained by Russian and North American regions seems relevant as it enables to identify risks of language management and use the positive experience of the studied regions to solve the tasks of language policy and language planning in the regions of the Russian Federation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".